Diabetes mellitus is a chronic metabolic disorder caused by an inability of the pancreas to produce sufficient amounts of the hormone insulin. This failure leads to hyperglycemia, i.e. the presence of an excessive amount of glucose in the blood plasma. Persistent hyperglycemia has been associated with a variety of serious symptoms and life threatening long term complications. Because restoration of endogenous insulin production is not yet possible, a permanent therapy is necessary which provides constant glycemic control in order to maintain the level of blood glucose within normal limits. Such glycemic control is achieved by regularly supplying external insulin to the body of the patient.
Substantial improvements in glycemic control have been achieved by the development of drug delivery devices that allow for the delivery of drug in a manner that is similar to naturally occurring physiological processes and can be controlled to follow standard or individually modified protocols to give the patient better glycemic control.
The drug delivery devices can be constructed as implantable devices. Alternatively, an external device with an infusion set for subcutaneous infusion to the patient via the transcutaneous insertion of a catheter or cannula may be used. The external drug delivery devices are generally mounted on clothing or, and preferably, hidden beneath or inside clothing, or mounted on the body and are generally controlled via a user interface built-in to the device or on a separate remote control device.
The delivery of suitable amounts of insulin by the drug delivery device requires that the patient frequently determines his or her blood glucose level. This value is inputted into the external pumps or controller, to determine whether a suitable modification to the default or currently in-use insulin delivery protocol, i.e. dosage and timing, is needed. The determination of blood glucose concentration is typically performed by means of an episodic measuring device, such as a hand-held electronic meter, which receives blood samples via enzyme-based test strips and calculates the blood glucose value based on the enzymatic reaction.
Alternatively, a continuous glucose monitor (“CGM”) may be utilized with drug delivery devices to allow for closed loop control of the insulin that is being infused into the diabetic patients. To allow for closed-loop control of the infused insulin, autonomous modulation of the drug being delivered to the user is provided by a controller using one or more algorithms. For example, a proportional-integral-derivative (“PID”) controller may be utilized and can be tuned based on simple rules of metabolic models.
Alternatively, a model predictive controller (“MPC”) has been demonstrated to be more robust than PID because MPC proactively considers the near future effects of control changes, sometimes subject to constraints, in determining the output of the MPC, whereas PID typically involves only past outputs in determining future changes. Constraints can be implemented in the MPC controller such that a solution is in a confined “space”, meaning within imposed delivery limitations, is guaranteed and the system is prevented from exceeding a limit that has been reached.
Details of the MPC controllers, and variations on the MPC and mathematical models representing the complex interaction of glucose and insulin are shown and described in the following documents:
U.S. Pat. No. 7,060,059; U.S. Patent Application Nos. 2011/0313680, 2011/0257627, and 2014/0180240; International Publication WO 2012/051344; Percival et al., “Closed-Loop Control and Advisory Mode Evaluation of an Artificial Pancreatic β-Cell: Use of Proportional-Integral-Derivative Equivalent Model-Based Controllers,” J. Diabetes Sci. Techn., Vol. 2, Issue 4, July 2008; Paola Soru et al., “MPC Based Artificial Pancreas; Strategies for Individualization and Meal Compensation,” Annual Reviews in Control 36, p. 118-128 (2012); Cobelli et al., “Artificial Pancreas: Past, Present, Future,” Diabetes, Vol. 60, November 2011; Magni et al., “Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial,” J. Diabetes Sci. Techn., Vol. 3, Issue 5, September 2009; Lee et al., “A Closed-Loop Artificial Pancreas Using Model Predictive Control and a Sliding Meal Size Estimator,” J. Diabetes Sci. Techn., Vol. 3, Issue 5, September 2009; Lee et al., “A Closed-Loop Artificial Pancreas based on MPC: Human Friendly Identification and Automatic Meal Disturbance Rejection,” Proceedings of the 17th World Congress, The International Federation of Automatic Control, Seoul Korea Jul. 6-11, 2008; Magni et al., “Model Predictive Control of Type 1 Diabetes: An in Silico Trial,” J. Diabetes Sci. Techn., Vol. 1, Issue 6, November 2007; Wang et al., “Automatic Bolus and Adaptive Basal Algorithm for the Artificial Pancreatic β-Cell,” Diabetes Techn. Ther., Vol. 12, No. 11, 2010; Percival et al., “Closed-Loop Control of an Artificial Pancreatic β-Cell Using Multi-Parametric Model Predictive Control” Diabetes Research 2008; Kovatchev et al., “Control to Range for Diabetes: Functionality and Modular Architecture,” J. Diabetes Sci. Techn., Vol. 3, Issue 5, September 2009; and Atlas et al., “MD-Logic Artificial Pancreas System,” Diabetes Care, Vol. 33, No. 5, May 2010. All articles or documents cited in this application are hereby incorporated by reference into this application as if fully set forth herein.
The advent of autonomous-dosing, artificial pancreas (“AP”)-type devices in diabetes care necessarily creates data that is much more abundant and complex than that of traditional, non-AP insulin pumps. This added complexity may overwhelm users of the devices, as well as caregivers and health care practitioners (“HCPs”), especially in the absence of a suitable tool to assist in the interpretation of such data and in which the complete value of the AP dosing paradigm may be lost.